Computer technology is a large part of almost every aspect of modern living. Computers range from fast and large super computers used to tackle very complex calculations, such as those associated with weather prediction, molecular dynamics and fluid flow problems, to single chip units that may be found in many everyday appliances such as washing machines, VCR's and cars. A common feature of nearly all modern-day computers is that they use a binary system for both the instruction codes and the representation of the data on which the instructions operate. A further feature of modern digital computers is that, for any process, an explicit set of instructions is assembled to completely describe how data is to be manipulated from the time it is retrieved or accessed until a result is to be either used or stored in an appropriate location or device. For some processes, such as the storing and retrieving of large volumes of data, either as text (ASCII format) or as values (Binary format), digital computers are eminently suitable. However, there also exist many tasks for which the digital architecture is far from ideal, such as the modeling of complex interactions best described by differential equations. Real world processes involve macroscopic properties that are analog in that they can have any value within a continuous range (excluded are the special conditions under which quantitation of properties can be demonstrated). Therefore these analog values need to be converted into a digital format before they can be used in any computation.
More importantly, digital computers operate in a completely different way from the nervous system or brains of even the most simple of organisms. A great deal of literature has been written about, and research carried out on, the distinction between the modern digital and neural processing of information, with the further realization that the latter is intimately linked with an ability to learn about the external world. Today an increasing amount of effort is expended towards gaining an understanding of the neural process with the aim of creating a machine to combine the ability to learn by experience with its computational and interpretive ability.
Soon after the revolution in transistor electronics, machines were built that were able to accept analog voltage values representative of real world parameters and process these according to predetermined arrangement of summing, integrating and differentiating units. The output of these analog computers were also voltages, again representative of the resulting computation. Their single biggest drawback was that the circuit had to be reconfigured each time a change in the actual computation was necessitated. Even so, nearly all transducers measuring real world properties in use today are associated with some fixed analog signal processing capability, both linear and non-linear in terms of the transformations applied.